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Patent 2862804 Summary

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Claims and Abstract availability

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(12) Patent Application: (11) CA 2862804
(54) English Title: HIERARCHICAL INFORMATION EXTRACTION USING DOCUMENT SEGMENTATION AND OPTICAL CHARACTER RECOGNITION CORRECTION
(54) French Title: EXTRACTION HIERARCHIQUE D'INFORMATIONS A L'AIDE D'UNE SEGMENTATION DE DOCUMENT ET CORRECTION DE RECONNAISSANCE OPTIQUE DE CARACTERES
Status: Dead
Bibliographic Data
(51) International Patent Classification (IPC):
  • G06Q 10/10 (2012.01)
  • G06K 9/62 (2006.01)
  • G06F 17/20 (2006.01)
(72) Inventors :
  • STADERMANN, JAN (United States of America)
  • JAGER, DENIS (United States of America)
  • ZERNIK, URI (United States of America)
(73) Owners :
  • RECOMMIND, INC. (United States of America)
(71) Applicants :
  • RECOMMIND, INC. (United States of America)
(74) Agent: MARKS & CLERK
(74) Associate agent:
(45) Issued:
(86) PCT Filing Date: 2012-12-27
(87) Open to Public Inspection: 2013-08-01
Availability of licence: N/A
(25) Language of filing: English

Patent Cooperation Treaty (PCT): Yes
(86) PCT Filing Number: PCT/US2012/071876
(87) International Publication Number: WO2013/112260
(85) National Entry: 2014-07-25

(30) Application Priority Data:
Application No. Country/Territory Date
13/360,425 United States of America 2012-01-27

Abstracts

English Abstract

Systems, methods, and media for extracting and processing entity data included in an electronic document are provided herein. Methods may include executing one or more extractors to extract entity data within an electronic document based upon an extraction model for the document, selecting extracted entity data via one or more experts, each of the experts applying at least one business rule to organize at least a portion of the selected entity data into a desired format, and providing the organized entity data for use by an end user.


French Abstract

L'invention concerne des systèmes, des procédés et des supports d'extraction et de traitement de données d'entité incluses dans un document électronique. Les procédés peuvent comprendre l'exécution d'un ou plusieurs extracteurs pour extraire des données d'entité dans un document électronique sur la base d'un modèle d'extraction pour le document, la sélection de données d'entité extraites par l'intermédiaire d'un ou plusieurs experts, chacun des experts appliquant au moins une règle commerciale pour organiser au moins une partie des données d'entité sélectionnées dans un format souhaité, et la fourniture des données d'entité organisées pour une utilisation par un utilisateur final.

Claims

Note: Claims are shown in the official language in which they were submitted.


CLAIMS

What is claimed is:

1. A method for extracting entity data from electronic documents, the method
comprising:
executing one or more extractors to extract entity data within an electronic
document based upon an extraction model for the document;
selecting extracted entity data via one or more experts, each of the experts
applying at least one business rule to organize at least a portion of
the selected entity data into a desired format; and
providing the organized entity data for use by an end user.
2. The method according to claim 1, wherein the organized entity data are
arranged into an extensible markup language file.
3. The method according to claim 2, further comprising normalizing an entity
by
applying a normalization scheme to the entity.
4. The method according to claim 1, further comprising generating a user
interface that includes the organized entity data and a view of the electronic

document that includes an annotation for each of the extracted entities.
5. The method according to claim 1, wherein a layout for the electronic
document
defines a target section and one or more target entity data included in the
target
section that are to be extracted by the one or more extractors.
21


6. The method according to claim 1, wherein the at least one business rule
comprises a set of slots, wherein each slot comprises a property that defines
a
condition for filling the slot via an expert.
7. The method according to claim 6, further comprising filling a slot with an
extracted entity data when the extracted entity data matches the property for
the
slot.
8. The method according to claim 7, further comprising validating the slot
when
the slots of the set are filled with extracted entity data.
9. The method according to claim 1, further comprising preventing extraction
of
entity data from a section of the electronic document having distorted content
by:
generating a first-order hidden markov model for each section of the
document, based upon a layout of the document;
applying the first-order hidden markov model to a section of the electronic
document that includes distorted text to determine the most likely
hidden states for the section;
aligning the section with characters extracted from the section of the
electronic document; and
configuring the one or more extractors and the one more experts to ignore
at least a portion of the electronic document determined to include
distorted content, based upon the alignment.
22


10. A system for providing extracting entity data from electronic documents,
the
system comprising:
a memory for storing an executable instructions that extract entity data
from electronic documents;
a processor that executes the instructions;
an extraction module that extracts entity data within an electronic
document based upon an extraction model for the electronic
document;
an expert that selects extracted entity data and applies at least one
business rule to organize at least a portion of the selected entity
data into a desired format; and
an output generator that outputs the organized entities.
11. The system according to claim 10, wherein the output generator organizes
the
entity data into an extensible markup language file.
12. The system according to claim 10, wherein the output module generates a
user interface that includes the organized entity data and a view of the
electronic
document that includes an annotation for each of the extracted entity data.
13. The system according to claim 10, further comprising a normalization
module
that cooperates with the extraction module to normalize entity data by
applying
a normalization scheme to the entity data.
14. The system according to claim 10, wherein the layout defines a target
section
and one or more target entity data included in the target section that are to
be
extracted by the one or more extractors.
23


15. The system according to claim 10, wherein the business rule comprises a
set of
slots, wherein each slot comprises a property that defines a condition for
filling
the slot via an expert.
16. The system according to claim 15, wherein the expert fills a slot with
extracted
entity data when the extracted entity data matches the property for the slot.
17. The system according to claim 16, wherein the expert validates the slot
when
the slots of the set are filled with extracted entity data.
18. The system according to claim 17, wherein the expert generates a combined
set that includes a validated set and one or more additional slots which are
to be
filled.
19. The system according to claim 10, further comprising a disambiguation
module that prevents extraction of entity data from a section of the
electronic
document having distorted content by:
generating a first-order hidden markov model for each section of the
document, based upon a layout of the document;
applying the first-order hidden markov model to a section of the electronic
document that includes distorted text to determine the most likely
hidden states for the section;
aligning the section with characters extracted from the section of the
electronic document; and
configuring the one or more extractors and the one more experts to ignore
at least a portion of the electronic document determined to include
distorted content, based upon the alignment.
24



20. A non-transitory computer readable storage media having a program
embodied thereon, the program being executable by a processor to perform a
method for extracting entity data from electronic documents, the method
comprising:
executing one or more extractors to extract entity data within an electronic
document based upon an extraction model of the electronic
document;
selecting extracted entity data via one or more experts, each of the experts
applying at least one business rule to organize at least a portion of
the selected entity data into a desired format; and
providing the organized entity data for use by an end user.

Description

Note: Descriptions are shown in the official language in which they were submitted.


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HIERARCHICAL INFORMATION EXTRACTION USING DOCUMENT
SEGMENTATION AND OPTICAL CHARACTER RECOGNITION
CORRECTION
FIELD OF THE TECHNOLOGY
[0001] Embodiments of the disclosure relate to systems and methods that
extract information from scanned documents having a discernible or known
structure.
BACKGROUND OF THE DISCLOSURE
[0002] Optical character recognition (OCR) tools may be utilized to
recognize
and expose recognized characters in a scanned document. Oftentimes OCR
technologies can be used to convert a scanned document into a text file or
other
word processor compatible file formats. While OCR tools are known,
automatically extracting entity data (objects) from these scanned documents is
often a difficult undertaking, even with documents that utilize a standard
layout
or format. Additional difficulties may be encountered when scanning processes
obscure or blur text within the document, along with OCR character recognition

errors, such as when characters are mistakenly or erroneously recognized. For
example, when the characters of "r" and "n" exist next to one another they may
be
mistakenly recognized as "m." Exemplary recognition errors may arise due to
font characteristics applied to the characters, as well as other formatting
errors.

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SUMMARY OF THE DISCLOSURE
[0003] According to some embodiments, the present technology may be
directed to methods for extracting entity data from electronic documents by
(a)
executing one or more extractors to extract entity data within an electronic
document based upon an extraction model for the document; (b) selecting
extracted entity data via one or more experts, each of the experts applying at
least
one business rule to organize at least a portion of the selected entity data
into a
desired format; and (c) providing the organized entity data for use by an end
user.
[0004] According to other embodiments, the present technology may be
directed to systems for synthesizing a view of at least a portion of a file
system
backup. These systems may include: (a) a memory for storing an executable
instructions that extract entity data from electronic documents; (b) a
processor
that executes the instructions; (c) an extraction module that extracts entity
data
within an electronic document based upon an extraction model for the
electronic
document; (d) an expert that selects extracted entity data and applies at
least one
business rule to organize at least a portion of the selected entity data into
a
desired format; and (e) an output generator that outputs the organized
entities.
[0005] According to additional embodiments, the present technology may be
directed to computer readable storage media for synthesizing a view of at
least a
portion of a file system backup. The storage media may include a program
embodied thereon, the program being executable by a processor to perform a
method for extracting entity data from electronic documents by (a) executing
one
or more extractors to extract entity data within an electronic document based
upon an extraction model for the document; (b) selecting extracted entity data
via
one or more experts, each of the experts applying at least one business rule
to
organize at least a portion of the selected entity data into a desired format;
and (c)
providing the organized entity data for use by an end user.
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BRIEF DESCRIPTION OF THE DRAWINGS
[0006] The accompanying drawings, where like reference numerals refer to
identical or functionally similar elements throughout the separate views,
together
with the detailed description below, are incorporated in and form part of the
specification, and serve to further illustrate embodiments of concepts that
include
the claimed disclosure, and explain various principles and advantages of those

embodiments.
[0007] The methods and systems disclosed herein have been represented
where appropriate by conventional symbols in the drawings, showing only those
specific details that are pertinent to understanding the embodiments of the
present disclosure so as not to obscure the disclosure with details that will
be
readily apparent to those of ordinary skill in the art having the benefit of
the
description herein.
[0008] FIG. 1 illustrates an exemplary system for practicing aspects of the
present technology;
[0009] FIGs. 2A and 2B illustrates an exemplary scanned section of an OCR
processed document and an exemplary output of raw text extraction from the
OCR processed document, respectively;
[0010] FIG. 3 shows a schematic diagram of an exemplary document
processing application;
[0011] FIG. 4 is a block diagram of an exemplary entity extraction and
expert
process;
[0012] FIGS. 5-7 are diagrammatical views of an exemplary application of
one
or more business rules to extracted entity data;
[0013] FIG. 8 is an exemplary graphical user interface that includes
extracted
entity information that is used to populate a form, along with an annotated
view
of the electronic document.
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[0014] FIG. 9 is a flowchart of an exemplary method for extracted entity
data
from an electronic document; and
[0015] FIG. 10 illustrates an exemplary computing system that may be used
to
implement embodiments according to the present technology.
10


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DETAILED DESCRIPTION
[0016] In the following description, for purposes of explanation,
numerous
specific details are set forth in order to provide a thorough understanding of
the
disclosure. It will be apparent, however, to one skilled in the art, that the
disclosure may be practiced without these specific details. In other
instances,
structures and devices are shown at block diagram form only in order to avoid
obscuring the disclosure.
[0017] Generally speaking, the present technology is directed to
hierarchical
entity extraction using document segmentation, optical character recognition
(OCR) correction, and data extraction. The present technology makes use of
automatically extracted entity information and cross-checks between
classifiers
(experts) to increase the robustness (i.e. precision) of the extracted data.
Additionally, the use of data extractors increases the portability of the
present
technology to new domains (other classes of structured documents) and
accommodates for variations in the layout (due to real layout-differences or
OCR
text misplacements) of the documents.
[0018] In other words, systems and methods provided herein utilize data
extractors to extract individual entity data from a document and data experts
that
extract high-level information from the document by applying business rules to
data gathered by the data extractors and also to validate the data.
[0019] It will be understood that for purposes of brevity, the terms
electronic
document may be referred to synonymously as a "document." That is,
documents processed by the present technology include electronic versions of
documents.
[0020] The present technology may employ a set of data extractors that
extract
important pieces of information associated with entity data within scanned or
other types of electronic documents. The data extractors may utilize an
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extraction model for the document, which defines both the entity data that are
to
be extracted, and a layout or structure of the document that guides the
extractors
to the correct portions of the document. The structure of the document may
include information such as known sections and standard entities included in
such sections. Extraction may include a determination of entity data as well
as
annotation of the data, and may not in all instances include extraction.
[0021] The extracted entity data may be further processed by a set of
experts
(data organization/verification modules) that arrange, assemble, or piece
together
the extracted entity data according to a desired format. Advantageously, the
desired format may be determined by a business rule. Once assembled by the
experts, the extracted and arranged entity data may be presented to the user,
along with a view of the original document that includes annotations for each
entity that was extracted from the document.
[0022] The extracted entity may be presented to a reviewer via a user
interface
and after review the information may be transferred to the customer using an
agreed format, such as extensible markup language (XML).
[0023] In sum, the present technology leverages dynamic data-extractors
that
can be reused to extract and evaluate various pieces of higher-level
information
within an electronic document. Additionally, the present technology can be
adapted to new domains or extended very easily by adding/changing a specific
set of extractors. The present technology may also compensate for local OCR
distortions that appear in the specific piece of information (e.g. presenting
a "S"
for the digit "5").
[0024] FIG. 1 illustrates an exemplary system for practicing aspects of
the
present technology. The system 100 may include a document processing system
105 that may include one or more web servers, along with digital storage media

device such as databases. The document processing system 105 may also
function as a cloud-based computing environment that is configured to process
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electronic documents in accordance with various embodiments of the present
technology. Details regarding the operation of document processing system 105
will be discussed in greater detail with regard to FIG. 3.
[0025] In general, a cloud-based computing environment is a resource that
typically combines the computational power of a large grouping of processors
and/or that combines the storage capacity of a large grouping of computer
memories or storage devices. For example, systems that provide a cloud
resource
may be utilized exclusively by their owners, such as Googleim or Yahoo! 'I'M;
or
such systems may be accessible to outside users who deploy applications within
the computing infrastructure to obtain the benefit of large computational or
storage resources.
[0026] The cloud may be formed, for example, by a network of web servers,
with each web server (or at least a plurality thereof) providing processor
and/or
storage resources. These servers may manage workloads provided by multiple
users (e.g., cloud resource customers or other users). Typically, each user
places
workload demands upon the cloud that vary in real-time, sometimes
dramatically. The nature and extent of these variations typically depend on
the
type of business associated with the user.
[0027] A plurality of client devices 110a-n may communicatively couple
with
the document processing system 105 via a network connection 115. The network
connection 115 may include any one of a number of private and public
communications mediums such as the Internet. The client devices 110a-n may be
required to authenticate themselves with the document processing system 105
via credentials such as a username/password combination, or any other
authentication means that would be known to one of ordinary skill the art with
the present disclosure before them.
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[0028] According to some embodiments, an OCR engine 120 may transmit or
upload OCR processed electronic documents to the document processing system
105 for entity data extraction and subsequent processing.
[0029] As background, the electronic documents that are processed by the
present technology may include scanned versions of hardcopy documents or
electronic versions of documents that are stored in any number of electronic
file
formats such as portable document format (PDF), image file formats such as
tagged image file format (TIFF), and so forth. The electronic documents may
have been processed using optical character recognition (OCR) technologies, to
extract characters and words from the electronic document in their original
file
format. Oftentimes, scanning processes, file conversion errors, compression,
and/or font related errors may lead to blurring of text within an electronic
document. Blurred text within an electronic document may create erroneous
output when OCR technologies are applied to the electronic document. That is,
the correct text included in the document may be extracted by the OCR
technologies such that the OCR output does not correspond to the correct text.

As will be discussed in greater detail below, the present technology may
utilize
statistical analyses to disambiguate erroneously extracted OCR output to
ensure
that only correctly translated content is utilized.
[0030] An exemplary scanned section of an OCR document is shown in FIG.
2A, along with the corresponding textual information obtained from each
subsection, in FIG. 2B. The scanned segment 200 of FIG. 2A includes a
"threshold" section of a contract. The threshold section includes plurality of

different subsections such as "independent amount 205," "threshold 210," and
"minimum transfer amount 215." The scanned segment 200 also includes a
distorted section 220 that includes textual information that was blurred
during
the scanning process. While such textual information is not difficult to
interpret
for human readers, such is not the case for automatic text extraction systems.
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[0031] During OCR character extraction, each of the above-describe
sections
of the scanned document 200 is properly extracted except the distorted section

220. FIG. 2B illustrates captured text section 225 that includes textual
information
captured from OCR processing of distorted section 220. For example, the OCR
processing of distorted section 220 produces the following output:
[0032]
<text> provided that if an Event of Default, Potential
Event of Default, Termination Event, or Additional
T'rrnination Event has occurred and is continuing
with respect to a party, then the Minimum Transfer
Amount 111 respect of that party shall be zero,</text>
[0033] As can be seen, the OCR processing of distorted section 220 has
mistakenly processed the word "Termination" as "T'rrnination."
Accommodations for these types of distortions will be discussed in greater
detail
infra.
[0034] Additionally, the OCR processing of text section 210 produces
output
230 that includes the following error:
[0035]
<text italics="on">"T/zreslwld"</text> <text>means with
respect to Party A:</text>
[0036] The word "Threshold" has been recognized by the OCR processor and
generated as output 230 that includes "T/zreslwld," which may have been caused
by the word being italicized.
[0037] Similarly, the OCR processing of text section 215 produces the
output
235 that includes the following error:
[0038]
<text italics="on">"Jlinimum Transfer Amount"</text>
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[0039] The word "Minimum" has been recognized by the OCR processor and
generated as output 235 that includes "Jlinimum," which again, may have been
caused by the word being italicized or inconsistencies with the color contrast
of
the characters.
[0040] Initially, the client devices 110a-n or the OCR generator 120 may
upload electronic documents (in some embodiments, OCR processed documents)
to the document processing system 105. Once uploaded, the electronic
documents may be processed by the document processing system 105 via
execution of a document processing application 300, which is described in
greater
detail below with reference to FIG. 3.
[0041] FIG. 3 illustrates a block diagram of an exemplary document
processing application, hereinafter application 300, which is constructed in
accordance with the present disclosure. Generally speaking, the application
300
may execute one or more extractors to extract entity data within an electronic
document based upon an extraction model for the document, select extracted
entity data via one or more experts, wherein each of the experts applying at
least
one business rule to organize at least a portion of the selected entity data
into a
desired format, and also provide the organized entity data for use by an end
user.
[0042] The application 300 may comprise a plurality of modules such as a
user
interface module 305, an extraction module 310, a normalization module 315, an
expert module 320, a post-processing module 325, a disambiguation module 330,
and an output module 335. It is noteworthy that the application 300 may
include
additional modules, engines, or components, and still fall within the scope of
the
present technology. As used herein, the term "module" may also refer to any of
an application-specific integrated circuit ("ASIC"), an electronic circuit, a
processor (shared, dedicated, or group) that executes one or more software or
firmware programs, a combinational logic circuit, and/or other suitable
components that provide the described functionality. In other embodiments,

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individual modules of the application 300 may include separately configured
web servers.
[0043] The client nodes may interact with the application 300 via one or
more
graphical user interfaces that are generated by the interface module 305. The
graphical user interfaces may provide any number of mechanisms that allow the
end user to upload electronic documents, specify the type of data that is to
be
extracted from the uploaded documents, their desired output format (e.g., a
user
interface or an XML document), along with any other type of instructional
information that will be used by the present technology to process the
uploaded
electronic documents.
[0044] Once an electronic document has been uploaded into the document
processing system, the extraction module 310 may execute a plurality of
extractors to extract entity data from the electronic document.
[0045] Again, an extraction model may be specified that guides the
extractors
in extracting entity data from the electronic document. In some embodiments,
the "extraction" of entity data may include annotating or otherwise
identifying
entity data for subsequent processing. Also, each extractor may utilize a
library
that includes a fixed or dynamic set of entities, or of regular expressions,
such as
expressions commonly utilized in the document layout. As stated above, the
extraction model may be generated from a basic structural template or layout
for
a particular type of document. For example, the document may include a
standardized contractual document (e.g., layout) that complies with the
International Swaps and Derivatives Association (ISDA) master agreement
format, although one of ordinary skill in the art will appreciate that other
document formats may likewise be utilized in accordance with the present
technology. The extraction model may utilize the layout for the document to
predictively determine the sections that should be included in the document,
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potentially the hierarchical arrangement of the sections within the document,
and/or individual entity types that should be present within a section.
[0046] According to some embodiments, the extraction module 310 may
execute individual extractors that examine the OCR processed text of the
document and extract individual entity data from within the document. An
extractor may be executed to obtain a number, a currency phrase, a keyword, or

any other definable content. By way of non-limiting example, an extractor may
extract an entity information such as "minimum transfer amount" (see output
235 of FIG. 2) which includes an extracted value of "EUR250,000."
[0047] Once entity data has been extracted by one or more extractors,
utilizing
the extraction model, the values associated with the extracted entity data may
be
normalized by the normalization module 315. The normalization module 315
may convert or normalize extracted entity data, for example, by converting a
number value into an agreed format or converting a currency value into an
international organization for standardization (ISO) format. The types of
normalization that may be applied to an extracted entity may depend upon any
standard, conversion methodology, and/or schema chosen by the end user.
[0048] Regardless of whether the normalization module 315 processes
entity
data extracted by an individual extractor or a plurality of extractors, the
normalization module 315 may receive normalization or conversion formats from
one or more resources, as shown in FIG. 4. The resources may include
standardized data formats that may be utilized by the normalization module to
convert an entity into an accepted data format. For example, the extracted
entity
data of "EUR250,000" may be converted by the normalization module 315 to a
format such as "250.000 E."
[0049] After extraction and/or normalization (if necessary), the expert
module
320 may execute experts that further process the extracted entity data
obtained by
the extractors. The experts apply business rules to the extracted entity data
to
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arrange or organize the extracted entity data. In some embodiments, the
experts
extract high-level information from an electronic document.
[0050] According to some embodiments, a business rule may define the type
of information that a particular end user desires to obtain from an electronic
document. For example, the end user may only be interested in harvesting
payment terms from a contract and the relative obligations of the parties
regarding the payment terms. As such, one or more experts may be executed to
organize both party specific and payment specific entity data into a format
that is
acceptable to the end user.
[0051] In some embodiments, a business rule (guidelines for assembling
extracted entity data points) using a set of slots. It will be understood that
each
slot may include one or more properties that define conditions when the slot
is
allowed to be filled. In some instances, when all, or a predetermined number
of
slots has been filled, the expert may verify or validate the entity data. An
exemplary application of a business rule to assemble extracted entity data is
shown with regard to FIGS. 5-7.
[0052] FIG. 4 illustrates a block diagram of an exemplary entity
extraction and
expert process. The process 400 includes the execution of two extractors such
as
"Data Extractor 1" 405 and "Data Extractor M" 410. Data Extractor 405 is shown
as cooperating with a "Resource 1" 415 to obtain normalization information
that
may be utilized by the extraction module 310 or the normalization module 315
to
normalize entity data extracted by the Data Extractor 405.
[0053] After extraction of entity data, the expert module 320 may execute
a
plurality of experts such as "Expert 1" 420 and "Expert K" 425. Once the
Expert
420 has applied a business rule to assemble extracted entity data into a
desired
format, the Expert 420 may cooperate with "Resource L" 430 to obtain
validating
information that may be utilized to confirm the accuracy of the assembled
data.
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[0054] In some instances, an expert such as Expert 425 may incorporate
assembled and/or verified entity data in a subsequent processing of additional

entity data. In this example, the Expert 425 may request assembled and
verified
entity data from the Expert 420.
[0055] After processing by the experts, the assembled and/or verified
entity
data may be output by an output module 335 in any format desired, such as
output to an XML file or a user interface. In other embodiments, the assembled

and/or verified entity data may be directed to a post-processing module 325,
such
as the QA Module 435, where statistical analyses or accuracy scores may be
generated for the entity data.
[0056] FIGS. 5-7 are diagrammatical views of an exemplary application of
one
or more business rules to extracted entity data. FIG. 5 includes a section 505
of
text from the electronic document that includes an extracted entity 510 of
"Threshold," an extracted entity 515 of "means with respect to," an extracted
entity 520A of "Party A," and an extracted entity 520B of "Party B."
Extraction in
the example includes annotation of the entity data via highlighting.
[0057] An expert may apply a business rule that determines a threshold
definition relative to each party. The business rule is applied to the section
using
a set 525 that includes three slots 530A, 530B, and 530C. Slot 530A of
"Threshold" matches with the extracted entity 510 of "Threshold." Slot 530B is
descriptive of the defining term "Means," which specifies the definition of
the
"Threshold" entity for the section 505. Slot 530C is descriptive of "Each
party"
within the section 505. It is noteworthy to mention that each slot may include
one
or more properties that determine how the slot is to be filled. For example,
the
"Threshold" slot 530A includes the properties of "DISTANCE=40,"
"RESET_OTHER," AND "ORDER=1." The "DISTANCE=40" property will fill
the slot with the extracted entity data if the extracted entity data is within
a given
distance "40" to extracted entity data from already filled slots of the set.
It will be
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understood that the distance may be measured in characters. If the extracted
entity data is not within the specified distance property, the slot is
cleared. The
"RESET_OTHER" property specifies that if the current slot is filled, all other
slots
will be cleared. Finally, the "ORDER=1" property specifies that this slot may
only be filled if slots with a lower number (if any) are filled and slots with
a
higher number are not filled.
[0058] Each slot may have a different permutation of properties that are
based
upon the business rule applied. Other properties may include, but are not
limited to: "FINAL" that specifies that a slot is only to be filled once and
additional occurrences of the same extracted entity data are to be ignored;
"NON-FINAL" allows the slot to be overwritten; "GROUPIgroup identifier]
specifies that all slots within one group are treated as "filled" if at least
one slot of
the group is filled; "NOT_OVERLAPPING" requires that the extracted entity
value of a slot does not overlap with other slots of the same set; "OPTIONAL"
specifies that a slot is optional and may be counted as a "filled slot."
[0059] Another set 535 is shown as having the same slots as set 525, but
with
an additional slot 540 that includes a slot specifically for PartyA.
[0060] FIG. 6 illustrates exemplary output generated by an expert. In
this
illustration, the whole marked phrase (e.g., section 505 of FIG. 5) is the
extracted
entity data and an annotation is made over the whole span as defined by the
expert. That is, the expert defines that a threshold definition for each
party, such
as PartyA and PartyB.
[0061] FIG. 7 illustrates the subsequent use of assembled entity data
that was
generated by an expert. This assembled entity data may be utilized by another
expert and combined with other extracted entity data. For example, using the
ThresholdPartyA and ThresholdPartyB entity data assembled by a first expert, a

subsequent expert may combine these entity data points with another data point

such as "Amount."

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[0062] In this example, two sets 705 and 710 each include two slots. For
example, set 705 includes a slot 715 which includes previously assembled
entity
data ThresholdPartyA along with a slot 720 which includes an "Amount" entity
data extracted from the section. The second set 710 also includes two slots,
similarly to the first set 705 with the exception that the second set 710
utilizes the
ThresholdPartyB entity data. Two outputs are generated by this expert. The
first
output 725 includes "threshold_clause_a = Ratings-based" and the second output

730 includes "threshold_clause_b = Ratings-based."
[0063] In some embodiments, the present technology may be utilized to
generate table experts that produce special annotations to identify table
cells,
rather than text that appears in the body of a document. The same hierarchical

structure as utilized above may be applied to Table Experts. That is, table
cells
are comprised of extracted data. Additionally, a table row may be comprised of

cells and a table may be comprised of individual table rows.
[0064] Referring back to FIG. 3, the present technology may utilize
processes
and methods that reduce the extraction on errant data included in an
electronic
document. These processes may also be utilized to simplify the extraction
rules
utilized by the extractors and the experts.
[0065] In some instances the disambiguation module 330 may prevent the
extractors and experts from utilizing distorted content contained in the
document. The disambiguation module 330 may utilize hidden markov model
based segmentation using the aforementioned document layout of the document.
Generally speaking, these segmentation processes may identify paragraphs and
sub-sections which are known to exist in the document, but are distorted
during
scanning or other document processes.
[0066] The segmentation process may include representing segments of the
document by a first-order hidden markov model. For each level or section of
the
document, a separate model may be utilized. Each state within the model may
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represent a certain character with one additional state that covers all
characters
that are not of interest. This model may be applied to a target document using
a
Viterbi algorithm or any other algorithm that determines the most likely
hidden
states for a section with distorted text. The application of the Viterbi
algorithm
allows for alignment of sections to character in the electronic document.
Next,
the extractors and experts can be configured to process only certain sections
of
the document.
[0067] FIG. 8 illustrates an exemplary user interface 800 that includes
assembled entity data that has been extracted and utilized to populate a form
805
within a frame 810 of the user interface 800. A view 815 of the original
document
is shown in frame 820. The view 815 includes annotations (extractions of
entity
data by extractors) of the entity data that is included in the form 805. For
example, an entity of "party making the demand" entity 825 is highlighted in
the
view, as well as populating a field 830 within the form 805. Other entity data
may likewise be directly extracted or inferentially determined by an expert
and
used to populate one or more fields of the form 805.
[0068] FIG. 9 is a flowchart of an exemplary method 900 for extracting
entity
data from electronic documents. The method may include a step 905 of receiving

an electronic document. It will be understood that the electronic document may
include a document that has been scanned and processed via OCR technologies
to determine characters and text included in the document.
[0069] The method may also include a step 910 of defining and/or applying
an
extraction model that will be utilized as a guide to extract entity data from
the
document. The extraction model may be based upon a standard template or
format to which the document adheres.
[0070] The method may include a step 915 of executing one or more
extractors
to extract entity data within an electronic document based upon the extraction

model for the document.
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[0071] Next, the method may include a step 920 of selecting extracted
entity
data via one or more experts. It is noteworthy that each of the experts may
apply
at least one business rule to organize at least a portion of the selected
entity data
into a desired format.
[0072] After assembling or organizing the entity data, the method may
include a step 925 of providing the organized entity data for use by an end
user.
[0073] Step 925 may include storing the assembled entity data in an XML
file,
or displaying the assembled entity data in a user interface, along with a view
of
the file that has been annotated with the extracted entity data.
[0074] The computing system 1000 of FIG. 10 may be implemented in the
contexts of the likes of computing systems, networks, servers, or combinations

thereof. The computing system 1000 of FIG. 10 includes one or more processors
1100 and main memory 1200. Main memory 1200 stores, in part, instructions and
data for execution by processor 1100. Main memory 1200 may store the
executable code when in operation. The system 1000 of FIG. 10 further includes
a
mass storage device 1300, portable storage medium drive(s) 1400, output
devices
1500, user input devices 1600, a graphics display 1700, and peripheral devices

1800.
[0075] The components shown in FIG. 10 are depicted as being connected
via
a single bus 1900. The components may be connected through one or more data
transport means. Processor unit 1100 and main memory 1200 may be connected
via a local microprocessor bus, and the mass storage device 1300, peripheral
device(s) 1800, portable storage device 1400, and display system 1700 may be
connected via one or more input/output (I/O) buses.
[0076] Mass storage device 1300, which may be implemented with a magnetic
disk drive or an optical disk drive, is a non-volatile storage device for
storing
data and instructions for use by processor unit 1100. Mass storage device 1300
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may store the system software for implementing embodiments of the present
technology for purposes of loading that software into main memory 1200.
[0077] Portable storage device 1400 operates in conjunction with a
portable
non-volatile storage medium, such as a floppy disk, compact disk, digital
video
disc, or USB storage device, to input and output data and code to and from the
computing system 1000 of FIG. 10. The system software for implementing
embodiments of the present technology may be stored on such a portable
medium and input to the computing system 1000 via the portable storage device
1400.
[0078] Input devices 1600 provide a portion of a user interface. Input
devices
1600 may include an alphanumeric keypad, such as a keyboard, for inputting
alpha-numeric and other information, or a pointing device, such as a mouse, a
trackball, stylus, or cursor direction keys. Additionally, the system 1000 as
shown
in FIG. 10 includes output devices 1500. Suitable output devices include
speakers,
printers, network interfaces, and monitors.
[0079] Display system 1700 may include a liquid crystal display (LCD) or
other suitable display device. Display system 1700 receives textual and
graphical
information, and processes the information for output to the display device.
[0080] Peripherals 1800 may include any type of computer support device
to
add additional functionality to the computing system. Peripheral device(s)
1800
may include a modem or a router.
[0081] The components provided in the computing system 1000 of FIG. 10
are
those typically found in computing systems that may be suitable for use with
embodiments of the present technology and are intended to represent a broad
category of such computer components that are well known in the art. Thus, the
computing system 1000 of FIG. 10 may be a personal computer, hand held
computing system, telephone, mobile computing system, workstation, server,
minicomputer, mainframe computer, or any other computing system. The
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computer may also include different bus configurations, networked platforms,
multi-processor platforms, etc. Various operating systems may be used
including
Unix, Linux, Windows, Macintosh OS, Palm OS, Android, iPhone OS and other
suitable operating systems.
[0082] It is noteworthy that any hardware platform suitable for performing
the processing described herein is suitable for use with the technology.
Computer-readable storage media refer to any medium or media that participate
in providing instructions to a central processing unit (CPU), a processor, a
microcontroller, or the like. Such media may take forms including, but not
limited to, non-volatile and volatile media such as optical or magnetic disks
and
dynamic memory, respectively. Common forms of computer-readable storage
media include a floppy disk, a flexible disk, a hard disk, magnetic tape, any
other
magnetic storage medium, a CD-ROM disk, digital video disk (DVD), any other
optical storage medium, RAM, PROM, EPROM, a FLASHEPROM, any other
memory chip or cartridge.
[0083] While various embodiments have been described above, it should be
understood that they have been presented by way of example only, and not
limitation. The descriptions are not intended to limit the scope of the
technology
to the particular forms set forth herein. Thus, the breadth and scope of a
preferred embodiment should not be limited by any of the above-described
exemplary embodiments. It should be understood that the above description is
illustrative and not restrictive. To the contrary, the present descriptions
are
intended to cover such alternatives, modifications, and equivalents as may be
included within the spirit and scope of the technology as defined by the
appended claims and otherwise appreciated by one of ordinary skill in the art.
The scope of the technology should, therefore, be determined not with
reference
to the above description, but instead should be determined with reference to
the
appended claims along with their full scope of equivalents.

Representative Drawing
A single figure which represents the drawing illustrating the invention.
Administrative Status

For a clearer understanding of the status of the application/patent presented on this page, the site Disclaimer , as well as the definitions for Patent , Administrative Status , Maintenance Fee  and Payment History  should be consulted.

Administrative Status

Title Date
Forecasted Issue Date Unavailable
(86) PCT Filing Date 2012-12-27
(87) PCT Publication Date 2013-08-01
(85) National Entry 2014-07-25
Dead Application 2018-12-27

Abandonment History

Abandonment Date Reason Reinstatement Date
2017-12-27 FAILURE TO REQUEST EXAMINATION

Payment History

Fee Type Anniversary Year Due Date Amount Paid Paid Date
Application Fee $400.00 2014-07-25
Maintenance Fee - Application - New Act 2 2014-12-29 $100.00 2014-07-25
Maintenance Fee - Application - New Act 3 2015-12-29 $100.00 2015-12-21
Maintenance Fee - Application - New Act 4 2016-12-28 $100.00 2016-12-02
Maintenance Fee - Application - New Act 5 2017-12-27 $200.00 2017-11-22
Maintenance Fee - Application - New Act 6 2018-12-27 $200.00 2018-12-12
Owners on Record

Note: Records showing the ownership history in alphabetical order.

Current Owners on Record
RECOMMIND, INC.
Past Owners on Record
None
Past Owners that do not appear in the "Owners on Record" listing will appear in other documentation within the application.
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Document
Description 
Date
(yyyy-mm-dd) 
Number of pages   Size of Image (KB) 
Abstract 2014-07-25 2 73
Claims 2014-07-25 5 132
Drawings 2014-07-25 10 587
Description 2014-07-25 20 813
Representative Drawing 2014-07-25 1 27
Cover Page 2014-10-14 2 53
PCT Correspondence 2017-06-07 3 87
Prosecution-Amendment 2015-05-15 1 32
PCT 2014-07-25 10 531
Assignment 2014-07-25 4 129
Correspondence 2014-09-17 1 32
Correspondence 2014-10-10 2 50
Maintenance Fee Payment 2015-12-21 1 52